mobile feature-cloud panorama construction for image recognition applications
DESCRIPTION
Mobile feature-cloud panorama construction for image recognition applications Miguel Bordallo, Jari Hannuksela, Olli silvén Machine Vision Group University of Oulu. Contents. Introduction Image recognition applications Comparison of image-based context retrieval methods - PowerPoint PPT PresentationTRANSCRIPT
MACHINE VISION GROUP
MOBILE FEATURE-CLOUD PANORAMA CONSTRUCTION FOR IMAGE
RECOGNITION APPLICATIONS
Miguel Bordallo, Jari Hannuksela, Olli silvénMachine Vision Group
University of Oulu
MACHINE VISION GROUP
Contents
• Introduction• Image recognition applications
– Comparison of image-based context retrieval methods
• Context retrieval from video analysis• System design
– Application flow– Automatic start– Image registration– Moving-objects detection– Quality assesment
• Performance analysis• Conclusions
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Image-based context retrieval applications
•Point Your Camera to an object (landmark, poster)•Take a Picture•Get context information and display it
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Mobile context retrieval applications
Google googles
Snaptell
Kooaba
Nokia Point & Find
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Image recognition approaches
• Videos contain lots of information– Most of it redundant
• Image registration is easy – Smaller motions between frames– some frames can be discarded without
losing information
• Videos can capture wide angle scenes. – 3D world is better represented
• Transmission of compressed still image• Needs lots of storage in server• Image size implies large amount of data transmitted• Compression artifacts diminish quality
• Features extracted from still images• Amount of features needed not know beforehand• No feedback. Re-takes needed often• Two dimensional representation
• Feature-cloud extracted from video frames
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Still image vs. Video based
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Constructing a feature-cloud
Frame #1 Frame #16 Frame #31 Frame #46 Frame #61
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System design
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Application flow (client side)
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Automatic start of the application
•Recognizing characteristic motion patterns • Holding phone like a camera• Panning back and forth
•Reduces perceived latencies
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Interactive capture
VGA video analysis
Motion estimation system calculates shift, rotation and scale in real time
When frame is suitable for recognition (high quality), the user receives feedback and instructions
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Feature extraction & Image registration
• Feature extraction based on CHoG features• Compressed Histogram of Gradients
• Block Matching
• Best Linear Unbiased Estimator
• Compute registration parameters in real time to send to the server:
•Shift, rotation and change of scale
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Moving-objects detection
Object-detection ON Object-detection OFF
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Moving-objects detection
The features corresponding to a moving object are not sent to the server
Not-valid features are transmitted tothe server
Object-detection ON Object-detection OFF
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Quality assesment
Server receives only the features corresponding to high quality frames
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Performance comparison
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Summary
• Improve results in 3 dimensional environments• Interactivity• Detection of moving objects• Image quality assesment• Bigger field of view
• Reduce the communications need between clients and server
•Bandwidth reduction
• Reduce the workload of the servers